---
title: "The Essence of Outsider Music"
author: "River Vaudrin"
date: "8 March 2021"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
    css: style.css
---

```{r setup, include=FALSE}
# Load packages
library(tidyverse)
library(plotly) 
library(spotifyr)
library(ggrepel)
library(flexdashboard)
library(compmus)
library(ggplot2)


# Load entire discography 
snowden_disc <- get_artist_audio_features("12XwWwI5wcnQySACjbgf2y")
eilert_disc <- get_artist_audio_features("7aS6aXw9eYfVQxCG1RcVEK")
willis_disc <- get_artist_audio_features("50yovZJnAHoy0pBruvFaCR")
tim_disc <- get_artist_audio_features("0Nc79lkTxXLSIIHDB8mqJC")
shaggs_disc <- get_artist_audio_features("5ixdD9E7P9r51AOcSInQbl")
daniel_disc <- get_artist_audio_features("1jeYbk5eqo6wgsQPjLeU5w")

# Create new dataframe with all artists entire discography
disc <- list(snowden_disc, eilert_disc, willis_disc, tim_disc, shaggs_disc, daniel_disc) %>%
  reduce(full_join)


# Load specific albums
snowden_album <- subset(snowden_disc, album_name=="Life in the USA and Canada")
eilert_album <- subset(eilert_disc, album_name=="Eilert Is Back")
willis_album <- subset(willis_disc, album_name=="Rush Hour")
tim_album <- subset(tim_disc, album_name=="God Bless Tiny Tim")
shaggs_album <- subset(shaggs_disc, album_name=="Philosophy of the World" & album_id=="0TzI1YcMAQVdQ4yUZuy7fA")
daniel_album <- subset(daniel_disc, album_name=="Yip / Jump Music")

# Create new dataframe with all albums
albums <- list(snowden_album, eilert_album, willis_album, tim_album, shaggs_album, daniel_album) %>%
  reduce(full_join)

```

### Outsiders: **idiosyncratic** and **unconnected** from conventional art **by circumstance**.

Outsider music is a derivative of outsider art, which is defined as "any work of art produced by an untrained idiosyncratic artist who is typically unconnected to the conventional art world—not by choice but by circumstance." Its defining feature is "the tendency of the artists to be “straight talking” (even if that talk is straight from a radically different worldview)." In 2000, Irwin Chusid conceptualized the term outsider music in his book: Songs in the Key of Z, in which he explores the field of outsider music by highlighting its most prominent artists. Chusid defines outsider music as "crackpot and visionary music, where all trails lead essentially one place: over the edge." The book was accompanied by two compilation albums that featured a variety of outsider musicians. 

These definitions are quite vague, however, I think that we can make it less ambiguous if we look at the differences between outsider musicians and other idiosyncratic musicians that make unconventional music. The first difference is that outsider musicians create unconventional art by circumstance, and not by choice. "By circumstance" can be interpreted in multiple ways. One interpretation is easily noticeable from Chusid's categorization, and that is most of the artists have some kind of mental disorder. Tiny Tim had Asperger's syndrome, Wesley Willis was a schizophrenic, and Daniel Johnson was both schizophrenic and bipolar. In other words, their unconventionality stemmed from their disorders. Another interpretation is exemplified by The Shaggs, they were a band comprised of three teenage sisters in 1968 formed by their father. One day their father received news from his fortune teller mother that his daughters would become famous musicians, even though his daughters didn't have any experience in making music. Although, after hearing this prophecy he withdrew his daughters from school and sold some family belongings to buy his daughters' instruments, and so the band was formed. These kids essentially had no choice than to play music, and their inexperience shines trough on their first album Philosophy of the World. 

Another key difference that sets outsider musicians apart is the amateurish sound of their music. If the definition of an outsider musician is a musician that makes unconventional music by circumstance, then we could consider Beach Boys' Brian Wilson as an outsider musician, as he was, like Daniel Johnson, diagnosed with schizophrenia and bipolar disorder. However, Wilson's music is far more commercially successful than all of the outsider musicians mentioned by Chusid, I think that this is due to the fact that his music doesn't poses an amateurish quality. In summary, his means that the two defining features of outsider music are that it sounds amateuristic, and that it sounds unconventional because of the circumstances the musicians find themselves in. 

My corpus will consist of albums from artists that can be found on the compilation albums of Chusid's Songs in the Key of Z. The goal is to determine whether it is possible to categorize these outsider musicians on the basis of their computational properties. This will be done by analyzing the corpus with the track-based features provided by Spotify, alongside popular Music Information Retrieval (MIR) techniques.


![B.J. Snowden](images/snowden.jpg){width=20%}
![Eilart Pilram](images/eilart.jpg){width=20%}
![Wesley Willis](images/wesley.jpg){width=20%}

(From left-to-right: B.J. Snowden, Eilart Pilram, Wesley Willis)

![Tiny Tim](images/tiny.jpg){width=20%}
![The Shaggs](images/shaggs.jpg){width=20%}
![Daniel Johnston](images/daniel.jpg){width=20%}

(From left-to-right: Tiny Tim, The Shaggs, Daniel Johnson)


***
![(Songs in the Key of Z)](images/chusid.jpg){width=100%} 

**Corpus:**

 - Life In the USA and Canada, by B.J. Snowden
 - Eilart Is Back, by Eilert Pilram
 - Rush Hour, Wesley Willis
 - God Bless Tiny Tim, by Tiny Tim
 - Philosophy of the World, by The Shaggs
 - Yip! Jump Music, by Daniel Johnston
 
I decided to pick these albums mainly because they were created by outsider musicians that were classified as such by the person who introduced the concept of outsider music. However, I chose these particular artists from Chusid's book because I have listened to these albums and I actually find them very enjoyable.  
 
**Strengths and limitations of the corpus:**
_Strengths:_

 - The artists all have a very unique style of making music.
 - All of the albums have been classified as outsider music, without much debate. Except for that one time that B.J. Snowden criticized Chusid because she felt she did not belong on his list of outsiders.
 - Variety in styles might point to some interesting differences between these outsider musicians.
 
_Limitations:_

 - All of Wesley Willis' songs have the exact same structure, perhaps this could be valuable, who knows. 
 - Eilart Pilram is a Swedish Elvis impersonator, thus his album is entirely comprised of Elvis covers.

**Interesting songs**

 - In Canada, by B.J Snowden
 - Suspicious Minds, by Eilert Pilram
 - I Wipped Batman's Ass, by Wesley Willis
 - Livin' In the Sunlight, Lovin' In the Moon Light, by Tiny Tim
 - Who Are Parents?, by The Shaggs 
 - The Beatles, by Daniel Johnston

I've highlighted these songs for two reasons, (1) because they all possess a childlike quality, a feature that is often mentioned as a characteristic of outsider music; (2) and because I wanted to examine at least one song of every artist in the corpus in more detail. It will be interesting to see if the childlike quality would in some way be reflected in the metrics, valence and energy could show to be fruitful, as optimism, energy and positivity are common traits of childlike music. It might not be enough to confidently categorize outsider music, but it could be a starting point.



### Outsiders **cannot be generalized** trough mood.


```{r songmoodplot_1}
mean_valence_energy <- albums %>% 
  group_by(album_name) %>% 
  summarise(artist_name = artist_name,
            album_name = album_name,
            track_name = track_name,
            valence = mean(valence),
            energy  = mean(energy))


song_mood_plot <- ggplot(albums, aes(energy, valence, color=artist_name, shape=artist_name, label=track_name)) +
  geom_point(alpha=.45) +
  geom_point(data=mean_valence_energy, size=4) +
  geom_vline(xintercept=0.5, linetype="dotted") +
  geom_hline(yintercept=0.5, linetype="dotted") +
  theme_bw() +
  xlim(0,1) +
  ylim(0,1) +
  labs(
    title = "Song mood (corpus albums)",
    x = "Energy",
    y = "Valence",
    color = "Artist",
    shape = "Artist"
  )

song_mood_plot <- song_mood_plot + 
                 annotate("text", x=.25, y=.25, label="Sad", alpha=.6) +
                 annotate("text", x=.25, y=.75, label="Relaxed", alpha=.6) +
                 annotate("text", x=.75, y=.25, label="Angry", alpha=.6) +
                 annotate("text", x=.75, y=.75, label="Happy", alpha=.6) 
```


```{r}
mean_valence_energy_disc <- disc %>% 
  group_by(artist_name) %>% 
  summarise(artist_name = artist_name,
            album_name = album_name,
            track_name = track_name,
            valence = mean(valence),
            energy  = mean(energy))

song_mood_plot_disc <- ggplot(disc, aes(energy, valence, color=artist_name, shape=artist_name, label=track_name)) +
  geom_point(alpha=.25) +
  geom_point(data=mean_valence_energy_disc, size=4) +
  geom_vline(xintercept=0.5, linetype="dotted") +
  geom_hline(yintercept=0.5, linetype="dotted") +
  theme_bw() +
  xlim(0,1) +
  ylim(0,1) +
  labs(
    title = "Song mood (entire discogrophy)",
    x = "Energy",
    y = "Valence",
    color = "Artist",
    shape = "Artist"
  )

song_mood_plot_disc <- song_mood_plot_disc + 
                 annotate("text", x=.25, y=.25, label="Sad", alpha=.6) +
                 annotate("text", x=.25, y=.75, label="Relaxed", alpha=.6) +
                 annotate("text", x=.75, y=.25, label="Angry", alpha=.6) +
                 annotate("text", x=.75, y=.75, label="Happy", alpha=.6) 

```
![](images/moodplots.jpg){width=100%}


***

To examine if outsiders can be categorized by mood, I plotted the mean valence and energy of each album to determine the albums mood (relaxed, happy, angry or sad). Spotify defines these parameters as follows:

- Valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).

- Energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.

When we look at the results, we can observe that all albums are relatively close to the center of the plot. This means that we cannot confidently match any album to a certain mood. We can only conclude that Tiny Tim's album is less energetic than all of the other albums, and that Wesley Willis' album is more energetic than the other albums. Another interesting finding is that all of the albums are not overly positive or negative, all of the albums have a similar valence centered around 0.5.

Interestingly, for all of the artists, the mood of their albums (in the corpus) are consistent throughout their entire discographies. Thus the corpus is an accurate representation of the artists' entire music catalog. Another assumption we can make based on this outcome is, that the artists did not experiment a lot when it comes to valence and energy throughout their careers.
  
### The outsiders all sound **different**.

```{r cohesivenessplot, echo=FALSE, message=FALSE, warning=FALSE}
danceability_valence_energy_albums <- albums %>% 
  group_by(artist_name) %>% 
  summarise(artist_name = artist_name,
            album_name = album_name,
            track_name = track_name,
            score = valence + energy + danceability)

mean_std <- danceability_valence_energy_albums %>%
  group_by(album_name) %>%
  summarise(m = mean(score),
            s = sd(score)
  )

cohesivenessplot <- ggplot(danceability_valence_energy_albums, aes(x=album_name, y=score, color=artist_name, label=track_name)) + 
    geom_violin(alpha=.4) +
    geom_jitter(position=position_jitter(0.15), alpha=0.4) +
    #geom_boxplot(alpha=.2, width=.2) +
    ylim(0,3) +
    labs(title = "Album cohesiveness", x="Album name", y="Cohesiveness Score", color="Artist") +
    theme(axis.text.x = element_text(angle=40, hjust=1))

ggplotly(cohesivenessplot)
```

***

I introduce a coheisiveness score (comprised of descriptive scores rather than confidence scores) to determine the overall sound of each album, this score is defined as follows:

- cohesiveness score = danceability + valence + energy

- Where dancability is defined by Spotify as: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.

***

From these results we can observe a couple of findings. First, we can see that Wesley Willis's album is the most cohesive, meaning that all of his songs sound similar. This is no surprise when we take into account that all of his songs have the exact same song structure, alongside the fact that all of his basically sound the same, this will be futher explained in a latter section. 

Another finding is that Eilart Is Back, Godbless Tiny Tim and Yip Jump Music all have a high variance in cohesiveness score, meaning that they have a wide variety in songs (in the case of Eilart Pilarm's album this is not so strange, as it is essentially an Elvis compilation album). This is contrasted by Life In the USA and Canada and Philosophy of the World, which are albums that consist of songs that are more similar to eachother. 


### Some tracks aren't regular. (pt. 1)

```{r spokenwordsplot, echo=FALSE, message=FALSE, warning=FALSE}
spokenwordplot <- ggplot(albums, aes(x=album_name, y=speechiness, color=artist_name, label=track_name)) +
  geom_jitter(width=.3) +
  geom_text(aes(label=ifelse(speechiness>.66,as.character(track_name),'')),size=3) +
  geom_hline(yintercept=0.66, linetype="dotted", alpha=.6) +
  ylim(0,1) +
  labs(title = "Spoken word songs", x="Album name", y="Speechiness", color="Artist") +
  theme(axis.text.x = element_text(angle=40, hjust=1))

ggplotly(spokenwordplot)

```

***

Next I wanted to determine what songs are outliers. When we look at speechiness, which Spotify defines as:

- Speechiness: detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.

We can see that only three songs in the entire corpus are spoken word songs according to Spotify. Eilart Pilram, B.J. Snowden, The Shaggs, and Wesley Willis all refrained from making spoken word tracks.

### Some tracks aren't regular. (pt. 2)

```{r instrumentalplot, echo=FALSE, message=FALSE, warning=FALSE}
instrumentalplot <- ggplot(albums, aes(x=album_name, y=instrumentalness, color=artist_name, label=track_name)) +
  geom_jitter(width=.3) +
#  geom_text_repel(data=filter(albums, instrumentalness>0.5), aes(label=track_name), size=3) +
  geom_hline(yintercept=0.5, linetype="dotted", alpha=.6) +
  geom_text(aes(label=ifelse(instrumentalness>.5,as.character(track_name),'')),size=3,hjust=1,vjust=-.5) +
  ylim(0,1) +
  labs(title = "Instrumental songs", x="Album name", y="Instrumentalness", color="Artist") +
  theme(axis.text.x = element_text(angle=40, hjust=1))

ggplotly(instrumentalplot)
```

***
When it comes to instrumentalness, which Spotify defines as:

- Instrumentalness: predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.

We can see that six songs in the entire corpus are instrumental songs songs according to Spotify. Eilart Pilram, The Shaggs, and Wesley Willis all refrained from making instrumental music.

### The **Swedish Elvis** is pretty similar to his American counterpart. (pt. 1)

```{r}

# Eilert
sweden <-
  get_tidy_audio_analysis("0xjvEL8UXaEXulhVdvaAT7") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

# 452KBpASS5lZLeJWX9Ixub

# Elvis
us <-
  get_tidy_audio_analysis("6cw1OgKsuEWQbmQb5Z4a3T") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

compmus_long_distance(
  sweden %>% mutate(pitches = map(pitches, compmus_normalise, "chebyshev")),
  us %>% mutate(pitches = map(pitches, compmus_normalise, "chebyshev")),
  feature = pitches,
  method = "aitchison"
) %>%
  ggplot(
    aes(
      x = xstart + xduration / 2,
      width = xduration,
      y = ystart + yduration / 2,
      height = yduration,
      fill = d
    )
  ) +
  geom_tile() +
  coord_equal() +
  labs(title="Dynamic Time Warping: Silent Night",x = "Eilert Pilram", y = "Elvis Presley") +
  theme_minimal() +
  scale_fill_viridis_c(guide = NULL)

```

***

Dynamic Time Warping is a technique in MIR used to compare pitches of two versions of a song into one single visualisation. If the two versions are similar a diagonal line should become visible. Here we can see two renditions of _Silent Night_, this song was chosen in particular because it is the only song that has two versions with the exact same runtime. Most of Eilert's versions are a bit shorter. Why you ask? perhaps I will explore this furter in the future. 

Anyhow, in the plot we can vaguely see a diagonal line, however, it is not 100% clear. If it was, that would mean that the songs are on the same pitch for every second of the track. In the case of _Silent Night_ this is not the case, but the fact that a diagnoal line is visible indicates that there is quite an *overlap in pitch between* the two versions. 


### The **Swedish Elvis** is pretty similar to his American counterpart. (pt. 2)
```{r}

elvis_playlist <- get_playlist_audio_features(username, "6Hv2UFYgxcBVNmmj7ZAYMp", authorization = get_spotify_access_token())
eilert_playlist <- get_playlist_audio_features(username, "28M4B7qFdrFKvl9XvtPB6W", authorization = get_spotify_access_token())


mean_elvis <- elvis_playlist %>% 
  summarise(artist_name = "Elvis Presley",
            valence = mean(valence),
            energy  = mean(energy),
            danceability = mean(danceability),
            acousticness = mean(acousticness),
            liveness = mean(liveness),
            instrumentalness = mean(instrumentalness),
            speechiness = mean(speechiness)
            )

mean_eilert <- eilert_playlist %>%
  summarise(artist_name = "Eilert Pilram",
            valence = mean(valence),
            energy  = mean(energy),
            danceability = mean(danceability),
            acousticness = mean(acousticness),
            liveness = mean(liveness),
            instrumentalness = mean(instrumentalness),
            speechiness = mean(speechiness)
            )

radar_data <- list(mean_elvis, mean_eilert) %>%
  reduce(full_join)

fig <- plot_ly(
    type = 'scatterpolar',
    fill = 'toself'
  ) 
fig <- fig %>%
  add_trace(
    r = c(0.4376000, 0.4334933, 0.05243333, 0.7043733, 0.025044287, 0.1772533, 0.5453333),
    theta = c("Mean Danceability", "Mean Energy", "Mean Speechiness", "Mean Acousticness","Mean Instrumentalness", "Mean Liveness", "Mean Valence"),
    name = 'Elvis Presley'
  ) 
fig <- fig %>%
  add_trace(
    r = c(0.4501333, 0.4996000, 0.03138667, 0.4706067, 0.003415193, 0.1932133, 0.4265333),
    theta = c("Mean Danceability", "Mean Energy", "Mean Speechiness", "Mean Acousticness","Mean Instrumentalness", "Mean Liveness", "Mean Valence"),
    name = 'Eilert Pilram'
  ) 

fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0,1)
      )
    )
  ) 

fig
```

***

The radar plot shows the mean of every numeric Spotify parameter for _Eilert Is Back_ and a playlist comprised of Elvis songs that is identical to the tracklist of _Eilert Is Back_. The results show that there is **very little difference between Eilert and Elvis** their renditions. The only notable differences are that Eilert's versions are overall a bit more energetic and less positive. 

### The **Swedish Elvis** is pretty similar to his American counterpart. (pt. 3)

```{r}
elvis <-
  get_playlist_audio_features(
    username,
    "6Hv2UFYgxcBVNmmj7ZAYMp"
  ) %>%
  slice(1:30) %>%
  add_audio_analysis()
eilert <-
  get_playlist_audio_features(
    username,
    "28M4B7qFdrFKvl9XvtPB6W"
  ) %>%
  slice(1:30) %>%
  add_audio_analysis()


outsiders <-
  elvis %>%
  mutate(artist = "Elvis Presley") %>%
  bind_rows(eilert %>% mutate(artist = "Eilert Pilram"))


outsiders %>%
  mutate(
    timbre =
      map(
        segments,
        compmus_summarise,
        timbre,
        method = "mean"
      )
  ) %>%
  select(artist, timbre) %>%
  compmus_gather_timbre() %>%
  ggplot(aes(x = basis, y = value, fill = artist)) +
  geom_violin() +
  scale_fill_viridis_d() +
  labs(x = "Spotify Timbre Coefficients", y = "", fill = "Artist")
```

***
When we look at Spotify's Timbre coefficients for both Eilert and Elvis, we can observe two things. The first is that the mean of all of the coefficients is relatively similar for both artists. Second is that the variance, for the c02, c03, c04 and c05 is higher for Elvis.


### The Shaggs a band of three, each playing a different song (pt.1)

```{r eval=FALSE, include=FALSE}
circshift <- function(v, n) {
  if (n == 0) v else c(tail(v, n), head(v, -n))
}

#      C     C#    D     Eb    E     F     F#    G     Ab    A     Bb    B
major_chord <-
  c(   1,    0,    0,    0,    1,    0,    0,    1,    0,    0,    0,    0)
minor_chord <-
  c(   1,    0,    0,    1,    0,    0,    0,    1,    0,    0,    0,    0)
seventh_chord <-
  c(   1,    0,    0,    0,    1,    0,    0,    1,    0,    0,    1,    0)

major_key <-
  c(6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88)
minor_key <-
  c(6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17)

chord_templates <-
  tribble(
    ~name, ~template,
    "Gb:7", circshift(seventh_chord, 6),
    "Gb:maj", circshift(major_chord, 6),
    "Bb:min", circshift(minor_chord, 10),
    "Db:maj", circshift(major_chord, 1),
    "F:min", circshift(minor_chord, 5),
    "Ab:7", circshift(seventh_chord, 8),
    "Ab:maj", circshift(major_chord, 8),
    "C:min", circshift(minor_chord, 0),
    "Eb:7", circshift(seventh_chord, 3),
    "Eb:maj", circshift(major_chord, 3),
    "G:min", circshift(minor_chord, 7),
    "Bb:7", circshift(seventh_chord, 10),
    "Bb:maj", circshift(major_chord, 10),
    "D:min", circshift(minor_chord, 2),
    "F:7", circshift(seventh_chord, 5),
    "F:maj", circshift(major_chord, 5),
    "A:min", circshift(minor_chord, 9),
    "C:7", circshift(seventh_chord, 0),
    "C:maj", circshift(major_chord, 0),
    "E:min", circshift(minor_chord, 4),
    "G:7", circshift(seventh_chord, 7),
    "G:maj", circshift(major_chord, 7),
    "B:min", circshift(minor_chord, 11),
    "D:7", circshift(seventh_chord, 2),
    "D:maj", circshift(major_chord, 2),
    "F#:min", circshift(minor_chord, 6),
    "A:7", circshift(seventh_chord, 9),
    "A:maj", circshift(major_chord, 9),
    "C#:min", circshift(minor_chord, 1),
    "E:7", circshift(seventh_chord, 4),
    "E:maj", circshift(major_chord, 4),
    "G#:min", circshift(minor_chord, 8),
    "B:7", circshift(seventh_chord, 11),
    "B:maj", circshift(major_chord, 11),
    "D#:min", circshift(minor_chord, 3)
  )

key_templates <-
  tribble(
    ~name, ~template,
    "Gb:maj", circshift(major_key, 6),
    "Bb:min", circshift(minor_key, 10),
    "Db:maj", circshift(major_key, 1),
    "F:min", circshift(minor_key, 5),
    "Ab:maj", circshift(major_key, 8),
    "C:min", circshift(minor_key, 0),
    "Eb:maj", circshift(major_key, 3),
    "G:min", circshift(minor_key, 7),
    "Bb:maj", circshift(major_key, 10),
    "D:min", circshift(minor_key, 2),
    "F:maj", circshift(major_key, 5),
    "A:min", circshift(minor_key, 9),
    "C:maj", circshift(major_key, 0),
    "E:min", circshift(minor_key, 4),
    "G:maj", circshift(major_key, 7),
    "B:min", circshift(minor_key, 11),
    "D:maj", circshift(major_key, 2),
    "F#:min", circshift(minor_key, 6),
    "A:maj", circshift(major_key, 9),
    "C#:min", circshift(minor_key, 1),
    "E:maj", circshift(major_key, 4),
    "G#:min", circshift(minor_key, 8),
    "B:maj", circshift(major_key, 11),
    "D#:min", circshift(minor_key, 3)
  )
```

```{r eval=FALSE, include=FALSE}
philo <-
  get_tidy_audio_analysis("2WhRJwrsy57H5Mm1P3lykJ") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "mean", norm = "manhattan"
      )
  )

philo <- philo %>% 
  compmus_match_pitch_template(
    key_templates,         # Change to chord_templates if descired
    method = "manhattan",  # Try different distance metrics
    norm = "manhattan"     # Try different norms
  ) %>%
  ggplot(
    aes(x = start + duration / 2, width = duration, y = name, fill = d)
  ) +
  geom_tile() +
  scale_fill_viridis_c(guide = "none") +
  theme_minimal() +
  labs(title="Philosophy of the World", x = "Time (s)", y = "")

```

```{r eval=FALSE, include=FALSE}
foot <-
  get_tidy_audio_analysis("2IIcv6DEwEF6TRm29erm9O") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "mean", norm = "manhattan"
      )
  )

foot <- foot %>% 
  compmus_match_pitch_template(
    key_templates,         # Change to chord_templates if descired
    method = "manhattan",  # Try different distance metrics
    norm = "manhattan"     # Try different norms
  ) %>%
  ggplot(
    aes(x = start + duration / 2, width = duration, y = name, fill = d)
  ) +
  geom_tile() +
  scale_fill_viridis_c(guide = "none") +
  theme_minimal() +
  labs(title="My Pal Foot Foot", x = "Time (s)", y = "")
```

```{r eval=FALSE, include=FALSE}
wonder <-
  get_tidy_audio_analysis("4qKnTbpmQsoOR3JGtbc0Nd") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "mean", norm = "manhattan"
      )
  )

wonder <- wonder %>% 
  compmus_match_pitch_template(
    key_templates,         # Change to chord_templates if descired
    method = "manhattan",  # Try different distance metrics
    norm = "manhattan"     # Try different norms
  ) %>%
  ggplot(
    aes(x = start + duration / 2, width = duration, y = name, fill = d)
  ) +
  geom_tile() +
  scale_fill_viridis_c(guide = "none") +
  theme_minimal() +
  labs(title="Why Do I Feel?", x = "Time (s)", y = "")
```

```{r eval=FALSE, include=FALSE}
happy <-
  get_tidy_audio_analysis("02FJpM6Ob6u9Y6Vf9Giinj") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "mean", norm = "manhattan"
      )
  )

happy <- happy %>% 
  compmus_match_pitch_template(
    key_templates,         # Change to chord_templates if descired
    method = "manhattan",  # Try different distance metrics
    norm = "manhattan"     # Try different norms
  ) %>%
  ggplot(
    aes(x = start + duration / 2, width = duration, y = name, fill = d)
  ) +
  geom_tile() +
  scale_fill_viridis_c(guide = "none") +
  theme_minimal() +
  labs(title="I'm so Happy When You're Near", x = "Time (s)", y = "")
```

![](images/shaggsgram.jpg){width=100%}

***

The Shaggs have been described as a band consisting of three members, each playing a totally different song. An important factor is their drummer who plays a constant pattern throughout the entire track. Often the track opens and closes with the only the drummer playing, this is clearly visible in the keygrams of _Philosophy of the World_, _My Pal Foot Foot_, _Why Do I Feel?_ and _I'm so Happy When You're Near_. I the keygrams we can see that there usually is an activation of all keys in the beginning and end, this indicates that there is no single key observable. What the keygram does observe is the percussion, which is key-less, hence it spans over all the keys. 

### The Shaggs a band of three, each playing a different song (pt.2)
```{r eval=FALSE, include=FALSE}
world <- get_tidy_audio_analysis("2WhRJwrsy57H5Mm1P3lykJ")

word <- world %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(title="Philophy of the World", x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

```{r eval=FALSE, include=FALSE}
car <- get_tidy_audio_analysis("3lJZ5C1zbgUeYl8szngWJb")

car <- car %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(title="That Little Sports Car", x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

```{r eval=FALSE, include=FALSE}
do <- get_tidy_audio_analysis("2XEIsAqCiaXGoJTWA8niyB")

do <- do %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(title="Wat Should I Do?", x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

```{r eval=FALSE, include=FALSE}
sweet <- get_tidy_audio_analysis("0JXy7FKhnnewRDuhvkXpsH")

sweet <- sweet %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(title="Sweet Thing", x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

![](images/shaggsgram2.jpg){width=100%}

***
The fact that each member of the Shaggs is playing a different song is clearly visible form the tempograms of 


### **Daniel Johnston** is all over the place. (pt. 1)

```{r eval=FALSE, include=FALSE}
enter <-
  get_tidy_audio_analysis("4AiCNRPVLZcqxfKuIlAqKn") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

enter <- enter %>%
  mutate(pitches = map(pitches, compmus_normalise, "manhattan")) %>%
  compmus_gather_chroma() %>% 
  ggplot(
    aes(
      x = start + duration / 2,
      width = duration,
      y = pitch_class,
      fill = value
    )
  ) +
  geom_tile() +
  labs(title="Chromagram: Sorry Entertainer, Daniel Johnson", x = "Time (s)", y = NULL, fill = "Magnitude") +
  theme_minimal() +
  scale_fill_viridis_c()
```

```{r eval=FALSE, include=FALSE}
shoes <-
  get_tidy_audio_analysis("6Er74mDxRRp1sTbOhC1jD0") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

shoes <- shoes %>%
  mutate(pitches = map(pitches, compmus_normalise, "manhattan")) %>%
  compmus_gather_chroma() %>% 
  ggplot(
    aes(
      x = start + duration / 2,
      width = duration,
      y = pitch_class,
      fill = value
    )
  ) +
  geom_tile() +
  labs(title="Chromagram: Worried Shoes, Daniel Johnson", x = "Time (s)", y = NULL, fill = "Magnitude") +
  theme_minimal() +
  scale_fill_viridis_c()

```

![](images/daniel12.jpg){width=100%}

***

A common feature of Daniel Johnson's music is that his songs typically do not have a dominant pitch, this becomes clear when we look at some chromagrams. A chromagram is a plot of a song showing the pitch registered at every second. When a pitch is registered a yellow bar is plotted in the corresponding pitch class, its brightness is determined by the magnitude. When we look at these two chromagrams of _Sorry Entertainer_ and _Worried Shoes_, we can see that both tracks do not have a dominant pitch. The former has a heavy emphasis on the A and G#|Ab pitch and the latter on the B and F#|Gb. 

### **Daniel Johnston** is all over the place. (pt. 2)

```{r eval=FALSE, include=FALSE}
columbo <-
  get_tidy_audio_analysis("56CyJWZRpiOP4XyWpEsWUA") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

columbo <- columbo %>%
  mutate(pitches = map(pitches, compmus_normalise, "manhattan")) %>%
  compmus_gather_chroma() %>% 
  ggplot(
    aes(
      x = start + duration / 2,
      width = duration,
      y = pitch_class,
      fill = value
    )
  ) +
  geom_tile() +
  labs(title="Chromagram: Who Killed John Columbo, Wesley Willis", x = "Time (s)", y = NULL, fill = "Magnitude") +
  theme_minimal() +
  scale_fill_viridis_c()


```

```{r eval=FALSE, include=FALSE}
easy <-
  get_tidy_audio_analysis("3lJNL14w4Uwcs1hexQZSQf") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

easy <- easy %>%
  mutate(pitches = map(pitches, compmus_normalise, "manhattan")) %>%
  compmus_gather_chroma() %>% 
  ggplot(
    aes(
      x = start + duration / 2,
      width = duration,
      y = pitch_class,
      fill = value
    )
  ) +
  geom_tile() +
  labs(title="Chromagram: Living Is Easy, B.J. Snowden", x = "Time (s)", y = NULL, fill = "Magnitude") +
  theme_minimal() +
  scale_fill_viridis_c()
```


```{r eval=FALSE, include=FALSE}
halloween <-
  get_tidy_audio_analysis("54qgVl1i7Lpj5DuWbOd4Hi") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

halloween <- halloween %>%
  mutate(pitches = map(pitches, compmus_normalise, "manhattan")) %>%
  compmus_gather_chroma() %>% 
  ggplot(
    aes(
      x = start + duration / 2,
      width = duration,
      y = pitch_class,
      fill = value
    )
  ) +
  geom_tile() +
  labs(title="Chromagram: It's Halloween, The Shaggs", x = "Time (s)", y = NULL, fill = "Magnitude") +
  theme_minimal() +
  scale_fill_viridis_c()


```

```{r eval=FALSE, include=FALSE}
dad <-
  get_tidy_audio_analysis("79yudXe8vrL3vhbfnArJ9D") %>%
  select(segments) %>%
  unnest(segments) %>%
  select(start, duration, pitches)

dad <- dad %>%
  mutate(pitches = map(pitches, compmus_normalise, "manhattan")) %>%
  compmus_gather_chroma() %>% 
  ggplot(
    aes(
      x = start + duration / 2,
      width = duration,
      y = pitch_class,
      fill = value
    )
  ) +
  geom_tile() +
  labs(title="Chromagram: Daddy, Daddy What Is Heaven Like, Tiny Tim", x = "Time (s)", y = NULL, fill = "Magnitude") +
  theme_minimal() +
  scale_fill_viridis_c()


```

![](images/daniel3456.jpg){width=100%}

***

When we compare Daniel Johnson's chromagrams to some of his outsider peers', his unique quality becomes even more evident. When we look at these particular songs of B.J. Snowden, The Shaggs, and Tiny Tim, we can see that these all have a dominant pitch, which is G. Wesley Willis' _Who Killed John Columbo_ has a dominant pitch of A. 
Daniel Johnson had the highest variance in cohesiveness score, his chromagrams give us a 


### All **Wesley Willis** songs are the same. (pt. 1)

```{r, echo = FALSE, message = FALSE}

batman <-
  get_tidy_audio_analysis("2Od8Vrqg0SAqVTLMHCMmWU") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "acentre", norm = "manhattan"
      )
  ) %>%
  mutate(
    timbre =
      map(segments,
        compmus_summarise, timbre,
        method = "mean"
      )
  )
bind_rows(
  batman %>%
    compmus_self_similarity(pitches, "aitchison") %>%
    mutate(d = d / max(d), type = "Chroma"),
  batman %>%
    compmus_self_similarity(timbre, "euclidean") %>%
    mutate(d = d / max(d), type = "Timbre")
) %>%
  mutate() %>%
  ggplot(
    aes(
      x = xstart + xduration / 2,
      width = xduration,
      y = ystart + yduration / 2,
      height = yduration,
      fill = d
    )
  ) +
  geom_tile() +
  coord_fixed() +
  facet_wrap(~type) +
  scale_fill_viridis_c(guide = "none") +
  theme_classic() +
  labs(x = "", y = "")
```

### All **Wesley Willis** songs are the same. (pt. 2)

```{r, echo = FALSE, message = FALSE}

chronic <-
  get_tidy_audio_analysis("0W463BKzugSbTS6qCl1Vq9") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "acentre", norm = "manhattan"
      )
  ) %>%
  mutate(
    timbre =
      map(segments,
        compmus_summarise, timbre,
        method = "mean"
      )
  )
bind_rows(
  chronic %>%
    compmus_self_similarity(pitches, "aitchison") %>%
    mutate(d = d / max(d), type = "Chroma"),
  chronic %>%
    compmus_self_similarity(timbre, "euclidean") %>%
    mutate(d = d / max(d), type = "Timbre")
) %>%
  mutate() %>%
  ggplot(
    aes(
      x = xstart + xduration / 2,
      width = xduration,
      y = ystart + yduration / 2,
      height = yduration,
      fill = d
    )
  ) +
  geom_tile() +
  coord_fixed() +
  facet_wrap(~type) +
  scale_fill_viridis_c(guide = "none") +
  theme_classic() +
  labs(x = "", y = "")
```

### All **Wesley Willis** songs are the same. (pt. 3)

```{r, echo = FALSE, message = FALSE}

fiasco <-
  get_tidy_audio_analysis("28GmmpvCQxa88rZdEntcYS") %>%
  compmus_align(bars, segments) %>%
  select(bars) %>%
  unnest(bars) %>%
  mutate(
    pitches =
      map(segments,
        compmus_summarise, pitches,
        method = "acentre", norm = "manhattan"
      )
  ) %>%
  mutate(
    timbre =
      map(segments,
        compmus_summarise, timbre,
        method = "mean"
      )
  )
bind_rows(
  fiasco %>%
    compmus_self_similarity(pitches, "aitchison") %>%
    mutate(d = d / max(d), type = "Chroma"),
  fiasco %>%
    compmus_self_similarity(timbre, "euclidean") %>%
    mutate(d = d / max(d), type = "Timbre")
) %>%
  mutate() %>%
  ggplot(
    aes(
      x = xstart + xduration / 2,
      width = xduration,
      y = ystart + yduration / 2,
      height = yduration,
      fill = d
    )
  ) +
  geom_tile() +
  coord_fixed() +
  facet_wrap(~type) +
  scale_fill_viridis_c(guide = "none") +
  theme_classic() +
  labs(x = "", y = "")
```

### All outdider musicians are **unique**, this means that the genre outsider music **cannot be generalized** trough their track features. 

```{r eval=FALSE, include=FALSE}
sun <- get_tidy_audio_analysis("1PmXm1881bonBI1AlG5uaH")

sun %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

```{r eval=FALSE, include=FALSE}
tulips <- get_tidy_audio_analysis("1Vchex0xowRj9k59RLvRfo")

tulips %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```
```{r eval=FALSE, include=FALSE}
near <- get_tidy_audio_analysis("2WhRJwrsy57H5Mm1P3lykJ")

near %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) + 
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

```{r eval=FALSE, include=FALSE}
canada <- get_tidy_audio_analysis("25dIBpehsuaeHlALc09HBJ")

canada %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```
```{r eval=FALSE, include=FALSE}
jesus <- get_tidy_audio_analysis("2Od8Vrqg0SAqVTLMHCMmWU")

jesus %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = FALSE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()

```

```{r eval=FALSE, include=FALSE}
graveola %>%
  tempogram(window_size = 8, hop_size = 1, cyclic = TRUE) %>%
  ggplot(aes(x = time, y = bpm, fill = power)) +
  geom_raster() +
  scale_fill_viridis_c(guide = "none") +
  labs(x = "Time (s)", y = "Tempo (BPM)") +
  theme_classic()
```